class_docstr: Fuse aggregated embeddings from both incoming and outgoing
  directions before updating node embeddings.
  .. math::
     h_{i}^{0} = [ x_i \| \mathbf{0} ]
     a_{i, \vdash}^{t} = \sum_{j\in\mathcal{N}_{\vdash }(i)}
     W_{\vdash} h_{j}^{t}
     a_{i, \dashv}^{t} = \sum_{j\in\mathcal{N}_{\dashv }(i)}
     W_{\dashv} h_{j}^{t}
     e_{i}^{t} &= \sigma (W_{f}[a_{i, \vdash}^{t};a_{i, \dashv}^{t};
     a_{i, \vdash}^{t}*a_{i, \dashv}^{t};
     a_{i, \vdash}^{t}-a_{i, \dashv}^{t}])
     h_{i}^{t+1} = \mathrm{GRU}(e_{i}^{t}, h_{i}^{t})
  Attributes
  ----------
  input_size: int
      Input feature size.
  output_size: int
      Output feature size.
  n_etypes: int
      Number of edge types. Default: 1.
  bias: bool
      If True, adds a learnable bias to the output. Default: True.
  Attributes
  ----------
  input_size: int
      Input feature size.
  output_size: int
      Output feature size.
  n_etypes: int
      Number of edge types. Default: 1.
  bias: bool
      If True, adds a learnable bias to the output. Default: True.
docstr: Reinitialize learnable parameters.
docstr_tok: ["Reinitialize","learnable","parameters","."]
code: def reset_parameters(self):
      """Reinitialize learnable parameters."""
      gain = nn.init.calculate_gain("relu")
      self.gru.reset_parameters()
      nn.init.xavier_normal_(self.fuse_linear.weight, gain=gain)
  
      for linear in self.linears_in:
          nn.init.xavier_normal_(linear.weight, gain=gain)
          nn.init.zeros_(linear.bias)
  
      for linear in self.linears_out:
          nn.init.xavier_normal_(linear.weight, gain=gain)
          nn.init.zeros_(linear.bias)
  
imports: import dgl
  import dgl.function
  import torch
  import torch.nn
  from torch.nn import init
  from ...data.data import GraphData
  from .base import GNNBase, GNNLayerBase
label_desc: Use image node_id 2 for calling the BiFuseGGNNLayerConv obj's underlying member method code with example usage: obj.reset_parameters() without return types